Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations3801
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory322.1 B

Variable types

DateTime1
Numeric9
Categorical2

Alerts

Price On Flipkart has 63 (1.7%) zerosZeros
Price On Jiomart has 178 (4.7%) zerosZeros
Discount On Amazon has 95 (2.5%) zerosZeros
Discount On Flipkart has 184 (4.8%) zerosZeros
Discount On Jiomart has 208 (5.5%) zerosZeros

Reproduction

Analysis started2024-11-05 15:45:37.353535
Analysis finished2024-11-05 15:46:45.922340
Duration1 minute and 8.57 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Date
Date

Distinct169
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size59.4 KiB
Minimum2024-04-24 00:00:00
Maximum2024-10-09 00:00:00
2024-11-05T21:16:46.582968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:47.228619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Price On Amazon
Real number (ℝ)

Distinct322
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40813.674
Minimum0
Maximum199900
Zeros33
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size59.4 KiB
2024-11-05T21:16:47.904208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1249
Q12999
median18999
Q364999
95-th percentile148900
Maximum199900
Range199900
Interquartile range (IQR)62000

Descriptive statistics

Standard deviation48130.051
Coefficient of variation (CV)1.1792629
Kurtosis1.7535443
Mean40813.674
Median Absolute Deviation (MAD)17500
Skewness1.4988464
Sum1.5513277 × 108
Variance2.3165018 × 109
MonotonicityNot monotonic
2024-11-05T21:16:48.648789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1499 139
 
3.7%
22999 134
 
3.5%
13999 127
 
3.3%
7999 127
 
3.3%
18999 122
 
3.2%
3299 121
 
3.2%
6999 96
 
2.5%
58999 94
 
2.5%
2999 85
 
2.2%
64999 81
 
2.1%
Other values (312) 2675
70.4%
ValueCountFrequency (%)
0 33
0.9%
1058 2
 
0.1%
1079 1
 
< 0.1%
1099 50
1.3%
1100 2
 
0.1%
1149 22
 
0.6%
1199 66
1.7%
1249 72
1.9%
1269 52
1.4%
1299 46
1.2%
ValueCountFrequency (%)
199900 50
1.3%
194900 9
 
0.2%
189400 58
1.5%
160508 4
 
0.1%
154900 3
 
0.1%
151700 55
1.4%
150900 1
 
< 0.1%
149900 8
 
0.2%
148900 50
1.3%
146999 5
 
0.1%

Price On Flipkart
Real number (ℝ)

ZEROS 

Distinct284
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37617.612
Minimum0
Maximum199900
Zeros63
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size59.4 KiB
2024-11-05T21:16:49.329399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1249
Q12699
median18340
Q360999
95-th percentile127990
Maximum199900
Range199900
Interquartile range (IQR)58300

Descriptive statistics

Standard deviation44007.78
Coefficient of variation (CV)1.1698717
Kurtosis2.06959
Mean37617.612
Median Absolute Deviation (MAD)16841
Skewness1.5100675
Sum1.4298454 × 108
Variance1.9366847 × 109
MonotonicityNot monotonic
2024-11-05T21:16:50.590671image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1499 162
 
4.3%
6999 161
 
4.2%
58999 146
 
3.8%
31999 133
 
3.5%
14999 104
 
2.7%
2499 91
 
2.4%
1299 89
 
2.3%
2999 81
 
2.1%
2699 73
 
1.9%
61999 70
 
1.8%
Other values (274) 2691
70.8%
ValueCountFrequency (%)
0 63
1.7%
1099 48
1.3%
1149 17
 
0.4%
1199 30
 
0.8%
1249 50
1.3%
1298 7
 
0.2%
1299 89
2.3%
1349 9
 
0.2%
1379 14
 
0.4%
1399 58
1.5%
ValueCountFrequency (%)
199900 63
1.7%
148900 56
1.5%
144990 1
 
< 0.1%
139990 67
1.8%
127990 57
1.5%
124990 33
0.9%
119990 35
0.9%
102999 5
 
0.1%
101999 28
0.7%
101499 5
 
0.1%

Price On Jiomart
Real number (ℝ)

ZEROS 

Distinct107
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40425.83
Minimum0
Maximum189900
Zeros178
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size59.4 KiB
2024-11-05T21:16:52.140787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q12999
median18499
Q369600
95-th percentile148400
Maximum189900
Range189900
Interquartile range (IQR)66601

Descriptive statistics

Standard deviation47940.714
Coefficient of variation (CV)1.1858931
Kurtosis1.2976932
Mean40425.83
Median Absolute Deviation (MAD)17100
Skewness1.3919307
Sum1.5365858 × 108
Variance2.298312 × 109
MonotonicityNot monotonic
2024-11-05T21:16:53.502009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2999 327
 
8.6%
79900 204
 
5.4%
999 186
 
4.9%
0 178
 
4.7%
22999 128
 
3.4%
18999 124
 
3.3%
69900 116
 
3.1%
60900 116
 
3.1%
13899 109
 
2.9%
3299 102
 
2.7%
Other values (97) 2211
58.2%
ValueCountFrequency (%)
0 178
4.7%
899 1
 
< 0.1%
999 186
4.9%
1019 6
 
0.2%
1199 13
 
0.3%
1298 4
 
0.1%
1299 15
 
0.4%
1330 2
 
0.1%
1399 36
 
0.9%
1499 97
2.6%
ValueCountFrequency (%)
189900 48
1.3%
185300 64
1.7%
174900 14
 
0.4%
154900 27
 
0.7%
148400 90
2.4%
139900 7
 
0.2%
126900 15
 
0.4%
124900 96
2.5%
117900 4
 
0.1%
116900 3
 
0.1%

MRP On Amazon
Real number (ℝ)

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48048.825
Minimum2999
Maximum199900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.4 KiB
2024-11-05T21:16:54.427478image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2999
5-th percentile3999
Q19499
median23999
Q379900
95-th percentile159900
Maximum199900
Range196901
Interquartile range (IQR)70401

Descriptive statistics

Standard deviation50533.087
Coefficient of variation (CV)1.0517029
Kurtosis1.0848689
Mean48048.825
Median Absolute Deviation (MAD)18000
Skewness1.3256989
Sum1.8263358 × 108
Variance2.5535928 × 109
MonotonicityNot monotonic
2024-11-05T21:16:55.120083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
17999 285
 
7.5%
5999 259
 
6.8%
89900 255
 
6.7%
9999 190
 
5.0%
8999 162
 
4.3%
2999 152
 
4.0%
36999 150
 
3.9%
6999 131
 
3.4%
10499 130
 
3.4%
9499 130
 
3.4%
Other values (18) 1957
51.5%
ValueCountFrequency (%)
2999 152
4.0%
3999 84
 
2.2%
4999 114
3.0%
5999 259
6.8%
6999 131
3.4%
8999 162
4.3%
9499 130
3.4%
9999 190
5.0%
10499 130
3.4%
10999 127
3.3%
ValueCountFrequency (%)
199900 126
3.3%
159900 124
3.3%
134900 125
3.3%
109900 127
3.3%
89900 255
6.7%
89600 125
3.3%
79900 123
3.2%
69900 125
3.3%
69600 130
3.4%
59900 125
3.3%

MRP On Flipkart
Real number (ℝ)

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48048.825
Minimum2999
Maximum199900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.4 KiB
2024-11-05T21:16:55.781708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2999
5-th percentile3999
Q19499
median23999
Q379900
95-th percentile159900
Maximum199900
Range196901
Interquartile range (IQR)70401

Descriptive statistics

Standard deviation50533.087
Coefficient of variation (CV)1.0517029
Kurtosis1.0848689
Mean48048.825
Median Absolute Deviation (MAD)18000
Skewness1.3256989
Sum1.8263358 × 108
Variance2.5535928 × 109
MonotonicityNot monotonic
2024-11-05T21:16:56.500300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
17999 285
 
7.5%
5999 259
 
6.8%
89900 255
 
6.7%
9999 190
 
5.0%
8999 162
 
4.3%
2999 152
 
4.0%
36999 150
 
3.9%
6999 131
 
3.4%
10499 130
 
3.4%
9499 130
 
3.4%
Other values (18) 1957
51.5%
ValueCountFrequency (%)
2999 152
4.0%
3999 84
 
2.2%
4999 114
3.0%
5999 259
6.8%
6999 131
3.4%
8999 162
4.3%
9499 130
3.4%
9999 190
5.0%
10499 130
3.4%
10999 127
3.3%
ValueCountFrequency (%)
199900 126
3.3%
159900 124
3.3%
134900 125
3.3%
109900 127
3.3%
89900 255
6.7%
89600 125
3.3%
79900 123
3.2%
69900 125
3.3%
69600 130
3.4%
59900 125
3.3%

MRP On Jiomart
Real number (ℝ)

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47476.318
Minimum2999
Maximum193500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.4 KiB
2024-11-05T21:16:57.045008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2999
5-th percentile3999
Q19499
median23999
Q379600
95-th percentile154900
Maximum193500
Range190501
Interquartile range (IQR)70101

Descriptive statistics

Standard deviation49229.428
Coefficient of variation (CV)1.036926
Kurtosis0.90143824
Mean47476.318
Median Absolute Deviation (MAD)18000
Skewness1.2700198
Sum1.8045748 × 108
Variance2.4235366 × 109
MonotonicityNot monotonic
2024-11-05T21:16:57.615657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
17999 285
 
7.5%
5999 259
 
6.8%
89600 249
 
6.6%
9999 190
 
5.0%
8999 162
 
4.3%
2999 152
 
4.0%
36999 150
 
3.9%
6999 131
 
3.4%
89900 131
 
3.4%
10499 130
 
3.4%
Other values (18) 1962
51.6%
ValueCountFrequency (%)
2999 152
4.0%
3999 84
 
2.2%
4999 114
3.0%
5999 259
6.8%
6999 131
3.4%
8999 162
4.3%
9499 130
3.4%
9999 190
5.0%
10499 130
3.4%
10999 127
3.3%
ValueCountFrequency (%)
193500 126
3.3%
154900 124
3.3%
129800 125
3.3%
109600 127
3.3%
89900 131
3.4%
89600 249
6.6%
79600 123
3.2%
69900 125
3.3%
69600 130
3.4%
59900 125
3.3%

Discount On Amazon
Real number (ℝ)

ZEROS 

Distinct345
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.867514
Minimum-0.33482143
Maximum88.430361
Zeros95
Zeros (%)2.5%
Negative2
Negative (%)0.1%
Memory size59.4 KiB
2024-11-05T21:16:58.271289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.33482143
5-th percentile4.966642
Q111.26408
median20.690369
Q345.007501
95-th percentile86.12068
Maximum88.430361
Range88.765183
Interquartile range (IQR)33.743421

Descriptive statistics

Standard deviation25.382185
Coefficient of variation (CV)0.84982583
Kurtosis0.075760335
Mean29.867514
Median Absolute Deviation (MAD)11.036138
Skewness1.1273621
Sum113526.42
Variance644.25533
MonotonicityNot monotonic
2024-11-05T21:16:58.995868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.69036863 128
 
3.4%
22.22345686 127
 
3.3%
27.27520684 124
 
3.3%
17.39206052 122
 
3.2%
45.00750125 121
 
3.2%
0 95
 
2.5%
15.23132184 94
 
2.5%
30.0030003 94
 
2.5%
27.69855395 81
 
2.1%
50.01667222 74
 
1.9%
Other values (335) 2741
72.1%
ValueCountFrequency (%)
-0.3348214286 2
 
0.1%
0 95
2.5%
0.2250562641 43
1.1%
2.501250625 9
 
0.2%
3.126954346 3
 
0.1%
4.578502122 5
 
0.1%
4.966641957 59
1.6%
5.122312824 62
1.6%
5.128205128 52
1.4%
5.252626313 58
1.5%
ValueCountFrequency (%)
88.43036109 3
 
0.1%
88.24313813 2
 
0.1%
87.78753195 25
 
0.7%
87.7764196 2
 
0.1%
87.3776187 8
 
0.2%
87.23191466 22
 
0.6%
86.67629737 21
 
0.6%
86.64069902 52
1.4%
86.3248763 3
 
0.1%
86.12068008 72
1.9%

Discount On Flipkart
Real number (ℝ)

ZEROS 

Distinct319
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.602215
Minimum0
Maximum88.430361
Zeros184
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size59.4 KiB
2024-11-05T21:16:59.807404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.1223128
Q112.500313
median20.759337
Q346.009202
95-th percentile84.676075
Maximum88.430361
Range88.430361
Interquartile range (IQR)33.508889

Descriptive statistics

Standard deviation25.16166
Coefficient of variation (CV)0.82221696
Kurtosis-0.05822644
Mean30.602215
Median Absolute Deviation (MAD)10.858335
Skewness1.0600331
Sum116319.02
Variance633.10915
MonotonicityNot monotonic
2024-11-05T21:17:00.782846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 184
 
4.8%
30.0030003 161
 
4.2%
16.66759264 104
 
2.7%
13.51387875 102
 
2.7%
15.23132184 100
 
2.6%
50.01667222 79
 
2.1%
50.00833472 71
 
1.9%
46.00920184 69
 
1.8%
12.45153221 67
 
1.8%
45.00750125 60
 
1.6%
Other values (309) 2804
73.8%
ValueCountFrequency (%)
0 184
4.8%
4.545867806 1
 
< 0.1%
5.122312824 57
 
1.5%
6.279344859 5
 
0.1%
6.563959956 5
 
0.1%
6.879299562 56
 
1.5%
7.189262966 28
 
0.7%
7.346182357 33
 
0.9%
7.3671875 42
 
1.1%
7.64422202 5
 
0.1%
ValueCountFrequency (%)
88.43036109 11
 
0.3%
87.78753195 17
 
0.4%
87.23191466 17
 
0.4%
86.8512475 1
 
< 0.1%
86.67629737 5
 
0.1%
86.3248763 3
 
0.1%
86.12068008 49
1.3%
85.79850511 9
 
0.2%
85.56506278 60
1.6%
84.76997809 1
 
< 0.1%

Discount On Jiomart
Real number (ℝ)

ZEROS 

Distinct111
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.663099
Minimum0
Maximum88.898767
Zeros208
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size59.4 KiB
2024-11-05T21:17:01.465457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.825893
median20.002
Q340.008002
95-th percentile85.726532
Maximum88.898767
Range88.898767
Interquartile range (IQR)29.182109

Descriptive statistics

Standard deviation24.612055
Coefficient of variation (CV)0.88970707
Kurtosis0.45736863
Mean27.663099
Median Absolute Deviation (MAD)10.318333
Skewness1.1971167
Sum105147.44
Variance605.75324
MonotonicityNot monotonic
2024-11-05T21:17:02.101093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 208
 
5.5%
10.82589286 204
 
5.4%
88.89876653 154
 
4.1%
20.69036863 128
 
3.4%
17.39206052 124
 
3.3%
71.4353748 122
 
3.2%
12.5 116
 
3.1%
40.0080016 114
 
3.0%
22.77904328 109
 
2.9%
45.00750125 102
 
2.7%
Other values (101) 2420
63.7%
ValueCountFrequency (%)
0 208
5.5%
1.860465116 48
 
1.3%
2.222345686 4
 
0.1%
2.234206471 15
 
0.4%
2.702775751 49
 
1.3%
3.775038521 96
2.5%
4.196255649 90
2.4%
4.237726098 64
 
1.7%
5.555864215 20
 
0.5%
8.850364964 39
 
1.0%
ValueCountFrequency (%)
88.89876653 154
4.1%
87.3776187 7
 
0.2%
87.1553079 1
 
< 0.1%
86.67629737 6
 
0.2%
85.72653236 32
 
0.8%
85.56506278 2
 
0.1%
85.44077725 6
 
0.2%
82.86503477 7
 
0.2%
82.64027792 14
 
0.4%
82.11390673 52
 
1.4%

Product Name
Categorical

Distinct32
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size375.6 KiB
boAt Ultima Call Max Smart Watch
 
162
Motorola G64 5G (Ice Lilac, 8GB RAM, 128GB Storage)
 
157
Realme Buds T110
 
152
Motorola Edge 50 Pro 5G with 68W Charger (Luxe Lavender, 256 GB) (8 GB RAM)
 
150
Realme Buds Air 5
 
145
Other values (27)
3035 

Length

Max length75
Median length47
Mean length36.192318
Min length13

Characters and Unicode

Total characters137567
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
2nd rowVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
3rd rowVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
4th rowVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
5th rowVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey

Common Values

ValueCountFrequency (%)
boAt Ultima Call Max Smart Watch 162
 
4.3%
Motorola G64 5G (Ice Lilac, 8GB RAM, 128GB Storage) 157
 
4.1%
Realme Buds T110 152
 
4.0%
Motorola Edge 50 Pro 5G with 68W Charger (Luxe Lavender, 256 GB) (8 GB RAM) 150
 
3.9%
Realme Buds Air 5 145
 
3.8%
boat lunar seek 131
 
3.4%
Apple iPhone 14 Plus (128 GB) - Blue 131
 
3.4%
Apple iPhone 14 (128 GB) - Midnight 130
 
3.4%
boAt Enigma Z40 Smart Watch 130
 
3.4%
boAt Wave Sigma 130
 
3.4%
Other values (22) 2383
62.7%

Length

2024-11-05T21:17:02.761721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gb 2283
 
8.2%
1479
 
5.3%
iphone 1385
 
5.0%
apple 1385
 
5.0%
ram 1126
 
4.1%
5g 937
 
3.4%
15 749
 
2.7%
128 747
 
2.7%
blue 681
 
2.5%
8 652
 
2.4%
Other values (79) 16267
58.7%

Most occurring characters

ValueCountFrequency (%)
24572
 
17.9%
e 8092
 
5.9%
a 5890
 
4.3%
B 5530
 
4.0%
i 5350
 
3.9%
l 5334
 
3.9%
G 5310
 
3.9%
o 4970
 
3.6%
t 4285
 
3.1%
1 3659
 
2.7%
Other values (48) 64575
46.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 137567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
24572
 
17.9%
e 8092
 
5.9%
a 5890
 
4.3%
B 5530
 
4.0%
i 5350
 
3.9%
l 5334
 
3.9%
G 5310
 
3.9%
o 4970
 
3.6%
t 4285
 
3.1%
1 3659
 
2.7%
Other values (48) 64575
46.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 137567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
24572
 
17.9%
e 8092
 
5.9%
a 5890
 
4.3%
B 5530
 
4.0%
i 5350
 
3.9%
l 5334
 
3.9%
G 5310
 
3.9%
o 4970
 
3.6%
t 4285
 
3.1%
1 3659
 
2.7%
Other values (48) 64575
46.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 137567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
24572
 
17.9%
e 8092
 
5.9%
a 5890
 
4.3%
B 5530
 
4.0%
i 5350
 
3.9%
l 5334
 
3.9%
G 5310
 
3.9%
o 4970
 
3.6%
t 4285
 
3.1%
1 3659
 
2.7%
Other values (48) 64575
46.9%

Title
Categorical

Distinct32
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size552.7 KiB
boAt Ultima Call Max Smart Watch w/ 2" Big HD Display, Advanced BT Calling, 100+ Sports Modes, 10 Days Battery Life, Multiple Watch Faces, IP68, HR & SpO2, Sedentary Alerts(Cherry Blossom)
 
162
Motorola G64 5G (Ice Lilac, 8GB RAM, 128GB Storage)
 
157
Realme Buds T110 with AI ENC for calls, upto 38 hours of Playback and Fast Charging Bluetooth Headset (Punk Black, True Wireless)
 
152
Motorola Edge 50 Pro 5G with 68W Charger (Luxe Lavender, 256 GB) (8 GB RAM)
 
150
Realme Buds Air 5 In-ear Wirless Earphone, Upto 38 hrs of playtime, Fast charging, IPX5 Water Resistant, Google Fast Pair, Bluetooth v5.3, 45ms Super Low Latency, Supports Dolby Atmos, Smart Adaptive Noise Cancellation, Blue
 
145
Other values (27)
3035 

Length

Max length224
Median length130
Mean length83.909498
Min length30

Characters and Unicode

Total characters318940
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
2nd rowVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
3rd rowVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
4th rowVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
5th rowVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey

Common Values

ValueCountFrequency (%)
boAt Ultima Call Max Smart Watch w/ 2" Big HD Display, Advanced BT Calling, 100+ Sports Modes, 10 Days Battery Life, Multiple Watch Faces, IP68, HR & SpO2, Sedentary Alerts(Cherry Blossom) 162
 
4.3%
Motorola G64 5G (Ice Lilac, 8GB RAM, 128GB Storage) 157
 
4.1%
Realme Buds T110 with AI ENC for calls, upto 38 hours of Playback and Fast Charging Bluetooth Headset (Punk Black, True Wireless) 152
 
4.0%
Motorola Edge 50 Pro 5G with 68W Charger (Luxe Lavender, 256 GB) (8 GB RAM) 150
 
3.9%
Realme Buds Air 5 In-ear Wirless Earphone, Upto 38 hrs of playtime, Fast charging, IPX5 Water Resistant, Google Fast Pair, Bluetooth v5.3, 45ms Super Low Latency, Supports Dolby Atmos, Smart Adaptive Noise Cancellation, Blue 145
 
3.8%
boAt Lunar Seek with 1.39'' HD Display, Functional Crown & Bluetooth Calling Smartwatch (Active Black Strap, Free Size) 131
 
3.4%
Apple iPhone 14 Plus (128 GB) - Blue 131
 
3.4%
Apple iPhone 14 (128 GB) - Midnight 130
 
3.4%
boAt Enigma Z40 Smart Watch w/ 1.32" (3.3 cm) HD Display, Luxurious Metal Body Design, 100+ Sports Mode, Female Wellness, Built-in Games, HR & SpO2, IP67(Metal Black) 130
 
3.4%
boAt Wave Sigma 3 w/Turn-by-Turn Navigation, 2.01" (5.1 cm) HD Display, Bluetooth Calling, Crest+ OS, QR Tray, Watch Face Studio, Coins, Emergency SOS Smart Watch for Men & Women(Metal Grey) 130
 
3.4%
Other values (22) 2383
62.7%

Length

2024-11-05T21:17:03.419342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gb 2283
 
4.0%
2260
 
4.0%
iphone 1385
 
2.4%
apple 1385
 
2.4%
ram 1126
 
2.0%
5g 937
 
1.6%
bluetooth 870
 
1.5%
with 859
 
1.5%
black 844
 
1.5%
blue 826
 
1.4%
Other values (212) 44193
77.6%

Most occurring characters

ValueCountFrequency (%)
53607
 
16.8%
e 18037
 
5.7%
a 16784
 
5.3%
t 13914
 
4.4%
l 12901
 
4.0%
i 12236
 
3.8%
o 12187
 
3.8%
r 10983
 
3.4%
s 8898
 
2.8%
, 8784
 
2.8%
Other values (60) 150609
47.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 318940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
53607
 
16.8%
e 18037
 
5.7%
a 16784
 
5.3%
t 13914
 
4.4%
l 12901
 
4.0%
i 12236
 
3.8%
o 12187
 
3.8%
r 10983
 
3.4%
s 8898
 
2.8%
, 8784
 
2.8%
Other values (60) 150609
47.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 318940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
53607
 
16.8%
e 18037
 
5.7%
a 16784
 
5.3%
t 13914
 
4.4%
l 12901
 
4.0%
i 12236
 
3.8%
o 12187
 
3.8%
r 10983
 
3.4%
s 8898
 
2.8%
, 8784
 
2.8%
Other values (60) 150609
47.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 318940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
53607
 
16.8%
e 18037
 
5.7%
a 16784
 
5.3%
t 13914
 
4.4%
l 12901
 
4.0%
i 12236
 
3.8%
o 12187
 
3.8%
r 10983
 
3.4%
s 8898
 
2.8%
, 8784
 
2.8%
Other values (60) 150609
47.2%

Interactions

2024-11-05T21:16:34.047132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:38.489888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:47.520725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:55.584116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:01.561698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:09.838966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:16.260295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:22.379809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:27.890650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:34.970603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:39.234462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:48.600107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:56.276720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:02.376235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:10.680487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:17.545564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:23.002445image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:28.550284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:35.999015image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:40.050994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:50.074265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:57.100248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:03.552560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:11.328139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:18.262157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:23.620089image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:29.339821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:38.050843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:42.270724image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:51.181632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:57.820837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:04.465040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:12.089681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:18.951758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:24.182768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:30.137365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:39.524998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:43.294141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:52.062129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:58.477467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:05.743309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:12.895220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:19.660352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:24.732453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:30.949901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:40.437480image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:44.166643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:52.919641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:59.036144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:06.674777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:13.712755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:20.343966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:25.324115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:31.735450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:41.112099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:45.178066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:53.705190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:59.770721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:07.439337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:14.457327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:20.938622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:26.016720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:32.416066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:41.773716image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:45.901650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:54.349827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:00.390374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:08.270869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:15.027001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:21.435364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:26.595390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:32.859810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:42.416347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:46.656218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:15:54.954475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:00.947052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:08.983462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:15.635656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:21.900098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:27.205041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T21:16:33.309551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-11-05T21:16:43.825540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-05T21:16:45.391645image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DatePrice On AmazonPrice On FlipkartPrice On JiomartMRP On AmazonMRP On FlipkartMRP On JiomartDiscount On AmazonDiscount On FlipkartDiscount On JiomartProduct NameTitle
02024-08-01499904999949999.05499954999549999.1074389.0910749.091074Vivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium GreyVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
12024-08-02499904999949999.05499954999549999.1074389.0910749.091074Vivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium GreyVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
22024-08-03499904999949999.05499954999549999.1074389.0910749.091074Vivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium GreyVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
32024-08-04499904999949999.05499954999549999.1074389.0910749.091074Vivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium GreyVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
42024-08-05499904999949999.05499954999549999.1074389.0910749.091074Vivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium GreyVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
52024-08-06499904999949999.05499954999549999.1074389.0910749.091074Vivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium GreyVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
62024-08-07499904999949999.05499954999549999.1074389.0910749.091074Vivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium GreyVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
72024-08-08499904999949999.05499954999549999.1074389.0910749.091074Vivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium GreyVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
82024-08-09499904999949999.05499954999549999.1074389.0910749.091074Vivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium GreyVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
92024-08-10499904999949999.05499954999549999.1074389.0910749.091074Vivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium GreyVivo V40 Pro 5G 256 GB, 8 GB RAM, Titanium Grey
DatePrice On AmazonPrice On FlipkartPrice On JiomartMRP On AmazonMRP On FlipkartMRP On JiomartDiscount On AmazonDiscount On FlipkartDiscount On JiomartProduct NameTitle
5432024-09-3011991299999.089998999899986.67629785.56506388.898767boAt Ultima Call Max Smart WatchboAt Ultima Call Max Smart Watch w/ 2" Big HD Display, Advanced BT Calling, 100+ Sports Modes, 10 Days Battery Life, Multiple Watch Faces, IP68, HR & SpO2, Sedentary Alerts(Cherry Blossom)
5442024-10-0111991299999.089998999899986.67629785.56506388.898767boAt Ultima Call Max Smart WatchboAt Ultima Call Max Smart Watch w/ 2" Big HD Display, Advanced BT Calling, 100+ Sports Modes, 10 Days Battery Life, Multiple Watch Faces, IP68, HR & SpO2, Sedentary Alerts(Cherry Blossom)
5452024-10-02119912991199.089998999899986.67629785.56506386.676297boAt Ultima Call Max Smart WatchboAt Ultima Call Max Smart Watch w/ 2" Big HD Display, Advanced BT Calling, 100+ Sports Modes, 10 Days Battery Life, Multiple Watch Faces, IP68, HR & SpO2, Sedentary Alerts(Cherry Blossom)
5462024-10-03119912991199.089998999899986.67629785.56506386.676297boAt Ultima Call Max Smart WatchboAt Ultima Call Max Smart Watch w/ 2" Big HD Display, Advanced BT Calling, 100+ Sports Modes, 10 Days Battery Life, Multiple Watch Faces, IP68, HR & SpO2, Sedentary Alerts(Cherry Blossom)
5472024-10-04119912991199.089998999899986.67629785.56506386.676297boAt Ultima Call Max Smart WatchboAt Ultima Call Max Smart Watch w/ 2" Big HD Display, Advanced BT Calling, 100+ Sports Modes, 10 Days Battery Life, Multiple Watch Faces, IP68, HR & SpO2, Sedentary Alerts(Cherry Blossom)
5482024-10-05119912991199.089998999899986.67629785.56506386.676297boAt Ultima Call Max Smart WatchboAt Ultima Call Max Smart Watch w/ 2" Big HD Display, Advanced BT Calling, 100+ Sports Modes, 10 Days Battery Life, Multiple Watch Faces, IP68, HR & SpO2, Sedentary Alerts(Cherry Blossom)
5492024-10-06119912991199.089998999899986.67629785.56506386.676297boAt Ultima Call Max Smart WatchboAt Ultima Call Max Smart Watch w/ 2" Big HD Display, Advanced BT Calling, 100+ Sports Modes, 10 Days Battery Life, Multiple Watch Faces, IP68, HR & SpO2, Sedentary Alerts(Cherry Blossom)
5502024-10-07119912991199.089998999899986.67629785.56506386.676297boAt Ultima Call Max Smart WatchboAt Ultima Call Max Smart Watch w/ 2" Big HD Display, Advanced BT Calling, 100+ Sports Modes, 10 Days Battery Life, Multiple Watch Faces, IP68, HR & SpO2, Sedentary Alerts(Cherry Blossom)
5512024-10-08119912991299.089998999899986.67629785.56506385.565063boAt Ultima Call Max Smart WatchboAt Ultima Call Max Smart Watch w/ 2" Big HD Display, Advanced BT Calling, 100+ Sports Modes, 10 Days Battery Life, Multiple Watch Faces, IP68, HR & SpO2, Sedentary Alerts(Cherry Blossom)
5522024-10-09119912991299.089998999899986.67629785.56506385.565063boAt Ultima Call Max Smart WatchboAt Ultima Call Max Smart Watch w/ 2" Big HD Display, Advanced BT Calling, 100+ Sports Modes, 10 Days Battery Life, Multiple Watch Faces, IP68, HR & SpO2, Sedentary Alerts(Cherry Blossom)